Probability Theory for Machine Learning

Probability Theory for Machine Learning

Jesse Bettencourt

September 2018

Introduction to Machine Learning

CSC411

University of Toronto

Introduction to Notation

Motivation

Uncertainty arises through:

• Noisy measurements

• Finite size of data sets

• Ambiguity

• Limited Model Complexity

Probability theory provides a consistent framework for the

quantification and manipulation of uncertainty.

1

Sample Space

Sample space Ω is the set of all possible outcomes of an

experiment.

Observations ω ∈ Ω are points in the space also called sample

outcomes, realizations, or elements.

Events E ⊂ Ω are subsets of the sample space.

2

Sample Space Coin Example

In this experiment we flip a coin twice:

Sample space All outcomes Ω = HH,HT ,TH,TTObservation ω = HT valid sample since ω ∈ Ω

Event Both flips same E = HH,TT valid event since E ⊂ Ω

3

Probability

Probability

The probability of an event E, P(E ), satisfies three axioms:

1: P(E ) ≥ 0 for every E

2: P(Ω) = 1

3: If E1,E2, . . . are disjoint then

P(∞⋃i=1

Ei ) =∞∑i=1

P(Ei )

4

Joint and Conditional Probabilities

Joint Probability of A and B is denoted P(A,B)

Conditional Probability of A given B is denoted P(A|B).

• Assuming P(B) > 0, then P(A|B) = P(A,B)/P(B)

• Product Rule: P(A,B) = P(A|B)P(B) = P(B|A)P(A)

5

Conditional Example

60% of ML students pass the final and 45% of ML students pass

both the final and the midterm.

What percent of students who passed the final also passed the

midterm?

6

Conditional Example

60% of ML students pass the final and 45% of ML students pass

both the final and the midterm.

What percent of students who passed the final also passed the

midterm?

Reword: What percent passed the midterm given they passed the

final?

P(M|F ) = P(M,F )/P(F )

= 0.45/0.60

= 0.75

7

Independence

Events A and B are independent if P(A,B) = P(A)P(B)

Events A and B are conditionally independent given C if

P(A,B|C ) = P(B|A,C )P(A|C ) = P(B|C )P(A|C )

8

Marginalization and Law of Total Probability

Marginalization (Sum Rule)

P(X ) =∑Y

P(X ,Y )

Law of Total Probability

P(X ) =∑Y

P(X |Y )P(Y )

9

Bayes’ Rule

Bayes’ Rule

Bayes’ Rule:

P(A|B) =P(B|A)P(A)

P(B)

10

Bayes’ Rule

Bayes’ Rule:

P(A|B) =P(B|A)P(A)

P(B)

P(θ|x) =P(x |θ)P(θ)

P(x)

Posterior =Likelihood ∗ Prior

Evidence

Posterior ∝ Likelihood × Prior

11

Bayes’ Example

Suppose you have tested positive for a disease. What is the

probability you actually have the disease?

12

Bayes’ Example



This depends on accuracy and sensitivity of test and prior

probability of the disease:

P(T = 1|D = 1) = 0.95 (true positive)

P(T = 1|D = 0) = 0.10 (false positive)

P(D = 1) = 0.1 (prior)

So P(D = 1|T = 1) =?

13

Bayes’ Example





P(D = 1) = 0.1 (prior)

Use Bayes’ Rule:

P(D = 1|T = 1) =P(T = 1|D = 1)P(D = 1)

P(T = 1)

P(D = 1|T = 1) =0.95 ∗ 0.1

P(T = 1)

15

Bayes’ Example





P(D = 1) = 0.1 (prior)

P(D = 1|T = 1) =0.95 ∗ 0.1

P(T = 1)(Bayes’ Rule)

By Law of Total Probability

P(T = 1) =∑D

P(T = 1|D)P(D)

= P(T = 1|D = 1)P(D = 1) + P(T = 1|D = 0)P(D = 0)

= 0.95 ∗ 0.1 + 0.1 ∗ 0.90

= 0.18516

Bayes’ Example





P(D = 1) = 0.1 (prior)

P(T = 1) = 0.185 (from Law of Total Probability)

P(D = 1|T = 1) =0.95 ∗ 0.1

P(T = 1)

=0.95 ∗ 0.1

0.185

= 0.51

Probability you have the disease given you tested positive is 51%

17

Random Variables and Statistics

Random Variable

How do we connect sample spaces and events to data?

A random variable is a mapping which assigns a real number X (ω)

to each observed outcome ω ∈ Ω

For example, let’s flip a coin 10 times. X (ω) counts the number of

Heads we observe in our sequence. If ω = HHTHTHHTHT then

X (ω) = 6.

18

I.I.D.

Random variables are said to be independent and identically

distributed (i.i.d.) if they are sampled from the same probability

distribution and are mutually independent.

This is a common assumption for observations. For example, coin

flips are assumed to be iid.

19

Discrete and Continuous Random Variables

Discrete Random Variables

• Takes countably many values, e.g., number of heads

• Distribution defined by probability mass function (PMF)

• Marginalization: p(x) =∑

y p(x , y)

Continuous Random Variables

• Takes uncountably many values, e.g., time to complete task

• Distribution defined by probability density function (PDF)

• Marginalization: p(x) =∫y p(x , y)dy

20

Probability Distribution Statistics

Mean: First Moment, µ

E [x ] =∞∑i=1

xip(xi ) (univariate discrete r.v.)

E [x ] =

∫ ∞−∞

xp(x)dx (univariate continuous r.v.)

Variance: Second Moment, σ2

Var [x ] =

∫ ∞−∞

(x − µ)2p(x)dx

= E [(x − µ)2]

= E [x2]− E [x ]2

21

Gaussian Distribution

Univariate Gaussian Distribution

Also known as the Normal Distribution, N (µ, σ2)

N (x |µ, σ2) =1√

2πσ2exp− 1

2σ2(x − µ)2

22

Multivariate Gaussian Distribution

Multidimensional generalization of the Gaussian.

x is a D-dimensional vector

µ is a D-dimensional mean vector

Σ is a D × D covariance matrix with determinant |Σ|

N (x|µ,Σ) =1

(2π)D/21

|Σ|1/2exp−1

2(x − µ)TΣ−1(x − µ)

23

Covariance Matrix

Recall that x and µ are D-dimensional vectors

Covariance matrix Σ is a matrix whose (i , j) entry is the covariance

Σij = Cov(xi , xj)

= E [(xi − µi )(xj − µj)]

= E [(xixj)]− µiµj

so notice that the diagonal entries are the variance of each

elements.

The covariant matrix has the property that it is symmetric and

positive-semidefinite (this is useful for whitening).

24

Whitening Transform

Whitening is a linear transform that converts a d-dimensional

random vector x = (x1, . . . , xd)T

with mean µ = E [x] = (µ1, . . . , µd)T and

positive definite d × d covariance matrix Cov(x) = Σ

into a new random d-dimensional vector

z = (z1, . . . , zd)T = W x

with “white” covariance matrix, Cov(z) = I

The d × d covariance matrix W is called the whitening matrix.

Mahalanobis or ZCA whitening matrix: WZCA = Σ−12

25

Inferring Parameters

Inferring Parameters

We have data X and we assume it is sampled from some

distribution.

How do we figure out the parameters that ‘best’ fit that

distribution?

Maximum Likelihood Estimation (MLE)

θMLE = argmaxθ

P(X |θ)

Maximum a Posteriori (MAP)

θMAP = argmaxθ

P(θ|X )

26

MLE for Univariate Gaussian Distribution

We are trying to infer the parameters for a Univariate Gaussian

Distribution, mean (µ) and variance (σ2).

N (x |µ, σ2) =1√

2πσ2exp− 1

2σ2(x − µ)2

The likelihood that our observations x1, . . . , xN were generated by

a univariate Gaussian with parameters µ and σ2 is

Likelihood = p(x1 . . . xN |µ, σ2) =N∏i=1

1√2πσ2

exp− 1

2σ2(xi − µ)2

27


For MLE we want to maximize this likelihood, which is difficult

because it is represented by a product of terms

Likelihood = p(x1 . . . xN |µ, σ2) =N∏i=1

1√2πσ2

exp− 1

2σ2(xi − µ)2

So we take the log of the likelihood so the product becomes a sum

Log Likelihood = log p(x1 . . . xN |µ, σ2)

=N∑i=1

log1√

2πσ2exp− 1

2σ2(xi − µ)2

Since log is monotonically increasing max L(θ) = max log L(θ)

28


The log Likelihood simplifies to

L(µ, σ) =N∑i=1

log1√

2πσ2exp− 1

2σ2(xi − µ)2

= −1

2N log(2πσ2)−

N∑i=1

(xi − µ)2

2σ2

Which we want to maximize. How?

29


To maximize we take the derivatives, set equal to 0, and solve:

L(µ, σ) = −1

2N log(2πσ2)−

N∑i=1

(xi − µ)2

2σ2

Derivative w.r.t. µ, set equal to 0, and solve for µ

∂L(µ, σ)

∂µ= 0 =⇒ µ =

1

N

N∑i=1

xi

Therefore the µ that maximizes the likelihood is the average of the

data points.

Derivative w.r.t. σ2, set equal to 0, and solve for σ2

∂L(µ, σ)

∂σ2= 0 =⇒ σ2 =

1

N

N∑i=1

(xi − µ)2

30

MLE for a biased coin

Suppose we observe a single outcome from the toss of a biased

coin, which has probably θ of landing on heads.

log p(x |θ) = x log(θ) + (1− x) log(1− θ)

The MLE maximizes the log-likelihood,

θMLE = x

where x is 0 or 1. There is a 100% chance of observing the same

outcome again!

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θMLE = x

where x is 0 or 1.

There is a 100% chance of observing the same

outcome again!

31






θMLE = x

where x is 0 or 1. There is a 100% chance of observing the same

outcome again!

31

MAP for a biased coin

We can place a prior distribution on θ. In this case, θ ∼ Beta(2, 2)

(conjugate prior).

Then the posterior is,

p(θ|x) = Beta(x + 2, 3− x)

(Show this!)

Which gives the MAP estimate,

θMAP =x + 1

3

This is 1/3 if we see a tails and 2/3 if we see a heads.

Priors help us reach reasonable conclusions when we have limited

observations. MAP is consistent with MLE when we have infinite

observations.

32



(conjugate prior).


p(θ|x) = Beta(x + 2, 3− x)

(Show this!) Which gives the MAP estimate,

θMAP =x + 1

3




observations.

32



(conjugate prior).


p(θ|x) = Beta(x + 2, 3− x)

(Show this!) Which gives the MAP estimate,

θMAP =x + 1

3




observations.32

Probability Theory for Machine Learning

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